Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations108035
Missing cells422673
Missing cells (%)24.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 MiB
Average record size in memory128.0 B

Variable types

Text1
DateTime1
Numeric13
Categorical1

Alerts

AQI is highly overall correlated with AQI_Bucket and 7 other fieldsHigh correlation
AQI_Bucket is highly overall correlated with AQI and 1 other fieldsHigh correlation
Benzene is highly overall correlated with Toluene and 1 other fieldsHigh correlation
CO is highly overall correlated with AQI and 1 other fieldsHigh correlation
NH3 is highly overall correlated with AQI and 2 other fieldsHigh correlation
NO is highly overall correlated with AQI and 4 other fieldsHigh correlation
NO2 is highly overall correlated with AQI and 4 other fieldsHigh correlation
NOx is highly overall correlated with AQI and 3 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 7 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 4 other fieldsHigh correlation
Toluene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
Xylene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
PM2.5 has 21625 (20.0%) missing values Missing
PM10 has 42706 (39.5%) missing values Missing
NO has 17106 (15.8%) missing values Missing
NO2 has 16547 (15.3%) missing values Missing
NOx has 15500 (14.3%) missing values Missing
NH3 has 48105 (44.5%) missing values Missing
CO has 12998 (12.0%) missing values Missing
SO2 has 25204 (23.3%) missing values Missing
O3 has 25568 (23.7%) missing values Missing
Benzene has 31455 (29.1%) missing values Missing
Toluene has 38702 (35.8%) missing values Missing
Xylene has 85137 (78.8%) missing values Missing
AQI has 21010 (19.4%) missing values Missing
AQI_Bucket has 21010 (19.4%) missing values Missing
Benzene is highly skewed (γ1 = 21.61702016) Skewed
NOx has 4776 (4.4%) zeros Zeros
CO has 7280 (6.7%) zeros Zeros
Benzene has 12602 (11.7%) zeros Zeros
Toluene has 10455 (9.7%) zeros Zeros
Xylene has 6083 (5.6%) zeros Zeros

Reproduction

Analysis started2024-12-31 00:46:00.245581
Analysis finished2024-12-31 00:46:24.454861
Duration24.21 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct110
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:24.639610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters540175
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAP001
2nd rowAP001
3rd rowAP001
4th rowAP001
5th rowAP001
ValueCountFrequency (%)
dl007 2009
 
1.9%
dl033 2009
 
1.9%
dl021 2009
 
1.9%
dl013 2009
 
1.9%
dl008 2009
 
1.9%
up012 2009
 
1.9%
tn001 2009
 
1.9%
up014 2009
 
1.9%
tn004 2009
 
1.9%
tn003 2009
 
1.9%
Other values (100) 87945
81.4%
2024-12-30T18:46:25.010581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 171293
31.7%
1 48475
 
9.0%
L 47246
 
8.7%
D 47223
 
8.7%
3 23227
 
4.3%
2 21884
 
4.1%
T 15544
 
2.9%
A 14911
 
2.8%
4 14632
 
2.7%
K 13572
 
2.5%
Other values (19) 122168
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 171293
31.7%
1 48475
 
9.0%
L 47246
 
8.7%
D 47223
 
8.7%
3 23227
 
4.3%
2 21884
 
4.1%
T 15544
 
2.9%
A 14911
 
2.8%
4 14632
 
2.7%
K 13572
 
2.5%
Other values (19) 122168
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 171293
31.7%
1 48475
 
9.0%
L 47246
 
8.7%
D 47223
 
8.7%
3 23227
 
4.3%
2 21884
 
4.1%
T 15544
 
2.9%
A 14911
 
2.8%
4 14632
 
2.7%
K 13572
 
2.5%
Other values (19) 122168
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 171293
31.7%
1 48475
 
9.0%
L 47246
 
8.7%
D 47223
 
8.7%
3 23227
 
4.3%
2 21884
 
4.1%
T 15544
 
2.9%
A 14911
 
2.8%
4 14632
 
2.7%
K 13572
 
2.5%
Other values (19) 122168
22.6%

Date
Date

Distinct2009
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size844.2 KiB
Minimum2015-01-01 00:00:00
Maximum2020-07-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-30T18:46:25.143604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:25.276836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation  Missing 

Distinct22395
Distinct (%)25.9%
Missing21625
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean80.272571
Minimum0.02
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:25.418828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile13.02
Q131.88
median55.95
Q399.92
95-th percentile236.5655
Maximum1000
Range999.98
Interquartile range (IQR)68.04

Descriptive statistics

Standard deviation76.526403
Coefficient of variation (CV)0.95333189
Kurtosis10.624383
Mean80.272571
Median Absolute Deviation (MAD)29.07
Skewness2.5639235
Sum6936352.9
Variance5856.2903
MonotonicityNot monotonic
2024-12-30T18:46:25.569562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 27
 
< 0.1%
31.08 23
 
< 0.1%
24.83 22
 
< 0.1%
28.8 22
 
< 0.1%
34.1 21
 
< 0.1%
21.38 21
 
< 0.1%
29.75 21
 
< 0.1%
42.5 21
 
< 0.1%
39.88 20
 
< 0.1%
21.88 20
 
< 0.1%
Other values (22385) 86192
79.8%
(Missing) 21625
 
20.0%
ValueCountFrequency (%)
0.02 2
< 0.1%
0.04 1
< 0.1%
0.15 1
< 0.1%
0.16 1
< 0.1%
0.19 1
< 0.1%
0.2 1
< 0.1%
0.24 1
< 0.1%
0.25 1
< 0.1%
0.28 1
< 0.1%
0.32 1
< 0.1%
ValueCountFrequency (%)
1000 1
< 0.1%
999.99 1
< 0.1%
995 1
< 0.1%
992.67 1
< 0.1%
949.99 1
< 0.1%
917.77 1
< 0.1%
916.67 1
< 0.1%
914.94 1
< 0.1%
914.64 1
< 0.1%
894.75 1
< 0.1%

PM10
Real number (ℝ)

High correlation  Missing 

Distinct29575
Distinct (%)45.3%
Missing42706
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean157.96843
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:25.712159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile30.27
Q170.15
median122.09
Q3208.67
95-th percentile409.386
Maximum1000
Range999.99
Interquartile range (IQR)138.52

Descriptive statistics

Standard deviation123.41867
Coefficient of variation (CV)0.78128696
Kurtosis3.3830811
Mean157.96843
Median Absolute Deviation (MAD)61.83
Skewness1.6440481
Sum10319919
Variance15232.169
MonotonicityNot monotonic
2024-12-30T18:46:25.848176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 12
 
< 0.1%
71.05 12
 
< 0.1%
71.88 12
 
< 0.1%
56.6 11
 
< 0.1%
108.46 11
 
< 0.1%
55.66 11
 
< 0.1%
46.68 10
 
< 0.1%
134.83 10
 
< 0.1%
41.49 10
 
< 0.1%
108.77 10
 
< 0.1%
Other values (29565) 65220
60.4%
(Missing) 42706
39.5%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
< 0.1%
0.1 3
< 0.1%
0.12 3
< 0.1%
ValueCountFrequency (%)
1000 1
< 0.1%
985 2
< 0.1%
976.77 1
< 0.1%
960.98 1
< 0.1%
955.6 1
< 0.1%
952 1
< 0.1%
942 1
< 0.1%
938.5 1
< 0.1%
936.25 1
< 0.1%
933.05 1
< 0.1%

NO
Real number (ℝ)

High correlation  Missing 

Distinct11963
Distinct (%)13.2%
Missing17106
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean23.123424
Minimum0.01
Maximum470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:26.200284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.5
Q14.84
median10.29
Q324.98
95-th percentile93.766
Maximum470
Range469.99
Interquartile range (IQR)20.14

Descriptive statistics

Standard deviation34.491019
Coefficient of variation (CV)1.4916052
Kurtosis14.052937
Mean23.123424
Median Absolute Deviation (MAD)7.02
Skewness3.288711
Sum2102589.8
Variance1189.6304
MonotonicityNot monotonic
2024-12-30T18:46:26.330884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 81
 
0.1%
2.89 79
 
0.1%
2.93 79
 
0.1%
2.49 77
 
0.1%
0.73 77
 
0.1%
2.87 76
 
0.1%
3 76
 
0.1%
2.95 75
 
0.1%
3.99 75
 
0.1%
2.84 75
 
0.1%
Other values (11953) 90159
83.5%
(Missing) 17106
 
15.8%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.02 10
< 0.1%
0.03 4
 
< 0.1%
0.04 2
 
< 0.1%
0.06 3
 
< 0.1%
0.07 1
 
< 0.1%
0.08 3
 
< 0.1%
0.09 2
 
< 0.1%
0.1 4
 
< 0.1%
0.11 4
 
< 0.1%
ValueCountFrequency (%)
470 1
< 0.1%
437.85 1
< 0.1%
436.8 1
< 0.1%
429.77 1
< 0.1%
403.94 1
< 0.1%
390.68 1
< 0.1%
383.14 1
< 0.1%
382.44 1
< 0.1%
374.71 1
< 0.1%
373.9 1
< 0.1%

NO2
Real number (ℝ)

High correlation  Missing 

Distinct12050
Distinct (%)13.2%
Missing16547
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean35.24076
Minimum0.01
Maximum448.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:26.460735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile5.3935
Q115.09
median27.21
Q346.93
95-th percentile89.88
Maximum448.05
Range448.04
Interquartile range (IQR)31.84

Descriptive statistics

Standard deviation29.510827
Coefficient of variation (CV)0.83740609
Kurtosis11.060617
Mean35.24076
Median Absolute Deviation (MAD)14.41
Skewness2.3592871
Sum3224106.7
Variance870.88892
MonotonicityNot monotonic
2024-12-30T18:46:26.600195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 43
 
< 0.1%
17.58 39
 
< 0.1%
16.07 37
 
< 0.1%
20 37
 
< 0.1%
0.2 36
 
< 0.1%
9.14 36
 
< 0.1%
17.82 36
 
< 0.1%
9.47 36
 
< 0.1%
13.6 35
 
< 0.1%
9.68 35
 
< 0.1%
Other values (12040) 91118
84.3%
(Missing) 16547
 
15.3%
ValueCountFrequency (%)
0.01 4
< 0.1%
0.02 7
< 0.1%
0.03 9
< 0.1%
0.04 3
 
< 0.1%
0.05 3
 
< 0.1%
0.06 3
 
< 0.1%
0.07 7
< 0.1%
0.08 6
< 0.1%
0.09 7
< 0.1%
0.1 8
< 0.1%
ValueCountFrequency (%)
448.05 1
< 0.1%
397.77 1
< 0.1%
397.31 1
< 0.1%
394.04 1
< 0.1%
393.08 1
< 0.1%
369.03 1
< 0.1%
363.75 1
< 0.1%
362.73 1
< 0.1%
362.5 1
< 0.1%
362.21 1
< 0.1%

NOx
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct15608
Distinct (%)16.9%
Missing15500
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean41.195055
Minimum0
Maximum467.63
Zeros4776
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:26.734537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.97
median26.66
Q350.5
95-th percentile134.153
Maximum467.63
Range467.63
Interquartile range (IQR)36.53

Descriptive statistics

Standard deviation45.145976
Coefficient of variation (CV)1.0959076
Kurtosis8.4549145
Mean41.195055
Median Absolute Deviation (MAD)15.84
Skewness2.5397855
Sum3811984.5
Variance2038.1591
MonotonicityNot monotonic
2024-12-30T18:46:26.872633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4776
 
4.4%
6.24 601
 
0.6%
2.21 516
 
0.5%
9.05 37
 
< 0.1%
17.01 34
 
< 0.1%
16.56 34
 
< 0.1%
11.02 34
 
< 0.1%
15.75 34
 
< 0.1%
22.02 34
 
< 0.1%
15.09 33
 
< 0.1%
Other values (15598) 86402
80.0%
(Missing) 15500
 
14.3%
ValueCountFrequency (%)
0 4776
4.4%
0.01 7
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 13
 
< 0.1%
0.05 4
 
< 0.1%
0.06 2
 
< 0.1%
0.07 3
 
< 0.1%
0.08 2
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
467.63 1
< 0.1%
453.61 1
< 0.1%
442.69 1
< 0.1%
440.31 1
< 0.1%
434.9 1
< 0.1%
429.38 1
< 0.1%
402.27 1
< 0.1%
399.87 1
< 0.1%
395.86 1
< 0.1%
395.33 1
< 0.1%

NH3
Real number (ℝ)

High correlation  Missing 

Distinct9119
Distinct (%)15.2%
Missing48105
Missing (%)44.5%
Infinite0
Infinite (%)0.0%
Mean28.732875
Minimum0.01
Maximum418.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:27.003147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.85
Q111.9
median23.59
Q338.1375
95-th percentile70.23
Maximum418.9
Range418.89
Interquartile range (IQR)26.2375

Descriptive statistics

Standard deviation24.897797
Coefficient of variation (CV)0.86652648
Kurtosis22.771798
Mean28.732875
Median Absolute Deviation (MAD)12.6
Skewness3.2189192
Sum1721961.2
Variance619.90031
MonotonicityNot monotonic
2024-12-30T18:46:27.139228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.42 49
 
< 0.1%
6.29 42
 
< 0.1%
14.62 39
 
< 0.1%
6.28 38
 
< 0.1%
6.3 38
 
< 0.1%
6.31 36
 
< 0.1%
6.6 34
 
< 0.1%
10.42 33
 
< 0.1%
6.33 32
 
< 0.1%
10.07 32
 
< 0.1%
Other values (9109) 59557
55.1%
(Missing) 48105
44.5%
ValueCountFrequency (%)
0.01 4
 
< 0.1%
0.02 9
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 2
 
< 0.1%
0.06 6
 
< 0.1%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 23
< 0.1%
ValueCountFrequency (%)
418.9 1
< 0.1%
408.58 1
< 0.1%
379.32 1
< 0.1%
371.36 1
< 0.1%
365.68 1
< 0.1%
361.75 1
< 0.1%
356.73 1
< 0.1%
352.89 1
< 0.1%
349.25 1
< 0.1%
335.91 1
< 0.1%

CO
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct2352
Distinct (%)2.5%
Missing12998
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1.6057493
Minimum0
Maximum175.81
Zeros7280
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:27.263940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.53
median0.91
Q31.45
95-th percentile3.38
Maximum175.81
Range175.81
Interquartile range (IQR)0.92

Descriptive statistics

Standard deviation4.3695778
Coefficient of variation (CV)2.7212079
Kurtosis224.16889
Mean1.6057493
Median Absolute Deviation (MAD)0.43
Skewness12.197951
Sum152605.6
Variance19.09321
MonotonicityNot monotonic
2024-12-30T18:46:27.401119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7280
 
6.7%
0.64 713
 
0.7%
0.66 696
 
0.6%
0.7 688
 
0.6%
0.78 679
 
0.6%
0.74 673
 
0.6%
0.68 671
 
0.6%
0.6 662
 
0.6%
0.79 659
 
0.6%
0.76 656
 
0.6%
Other values (2342) 81660
75.6%
(Missing) 12998
 
12.0%
ValueCountFrequency (%)
0 7280
6.7%
0.01 96
 
0.1%
0.02 125
 
0.1%
0.03 54
 
< 0.1%
0.04 52
 
< 0.1%
0.05 73
 
0.1%
0.06 54
 
< 0.1%
0.07 45
 
< 0.1%
0.08 54
 
< 0.1%
0.09 59
 
0.1%
ValueCountFrequency (%)
175.81 1
< 0.1%
145.32 1
< 0.1%
134.85 1
< 0.1%
132.47 1
< 0.1%
132.07 1
< 0.1%
124.01 1
< 0.1%
119.68 1
< 0.1%
119.3 1
< 0.1%
118.02 1
< 0.1%
118 1
< 0.1%

SO2
Real number (ℝ)

Missing 

Distinct5801
Distinct (%)7.0%
Missing25204
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean12.257634
Minimum0.01
Maximum195.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:27.538203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.07
Q15.04
median8.95
Q314.92
95-th percentile32.54
Maximum195.65
Range195.64
Interquartile range (IQR)9.88

Descriptive statistics

Standard deviation12.984723
Coefficient of variation (CV)1.0593173
Kurtosis34.720632
Mean12.257634
Median Absolute Deviation (MAD)4.54
Skewness4.5812814
Sum1015312.1
Variance168.60304
MonotonicityNot monotonic
2024-12-30T18:46:27.682935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4 84
 
0.1%
3.38 80
 
0.1%
4 79
 
0.1%
5.86 78
 
0.1%
3.51 75
 
0.1%
3.28 75
 
0.1%
3.48 75
 
0.1%
6.42 74
 
0.1%
4.76 74
 
0.1%
6.99 74
 
0.1%
Other values (5791) 82063
76.0%
(Missing) 25204
 
23.3%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 1
 
< 0.1%
0.03 3
 
< 0.1%
0.04 6
< 0.1%
0.05 3
 
< 0.1%
0.06 8
< 0.1%
0.07 3
 
< 0.1%
0.08 10
< 0.1%
0.09 4
 
< 0.1%
0.1 6
< 0.1%
ValueCountFrequency (%)
195.65 1
< 0.1%
193.86 1
< 0.1%
187.02 1
< 0.1%
186.08 1
< 0.1%
182.39 1
< 0.1%
180.85 1
< 0.1%
179.18 1
< 0.1%
178.93 1
< 0.1%
178.63 1
< 0.1%
178.58 1
< 0.1%

O3
Real number (ℝ)

Missing 

Distinct11166
Distinct (%)13.5%
Missing25568
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean38.134836
Minimum0.01
Maximum963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:27.826174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile7.02
Q118.895
median30.84
Q347.14
95-th percentile81
Maximum963
Range962.99
Interquartile range (IQR)28.245

Descriptive statistics

Standard deviation39.128004
Coefficient of variation (CV)1.0260436
Kurtosis75.831363
Mean38.134836
Median Absolute Deviation (MAD)13.49
Skewness6.8444834
Sum3144865.5
Variance1531.0007
MonotonicityNot monotonic
2024-12-30T18:46:27.964053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.48 35
 
< 0.1%
22.94 34
 
< 0.1%
23.6 33
 
< 0.1%
22.5 33
 
< 0.1%
34.4 32
 
< 0.1%
25.64 31
 
< 0.1%
23.59 31
 
< 0.1%
19.12 31
 
< 0.1%
21.4 30
 
< 0.1%
21.28 29
 
< 0.1%
Other values (11156) 82148
76.0%
(Missing) 25568
 
23.7%
ValueCountFrequency (%)
0.01 4
 
< 0.1%
0.02 9
< 0.1%
0.03 4
 
< 0.1%
0.04 3
 
< 0.1%
0.05 4
 
< 0.1%
0.06 3
 
< 0.1%
0.07 3
 
< 0.1%
0.08 2
 
< 0.1%
0.09 3
 
< 0.1%
0.1 11
< 0.1%
ValueCountFrequency (%)
963 1
< 0.1%
868.2 1
< 0.1%
819.06 1
< 0.1%
777.67 1
< 0.1%
763.12 1
< 0.1%
757 1
< 0.1%
714.75 1
< 0.1%
705.32 1
< 0.1%
698.67 1
< 0.1%
694.96 1
< 0.1%

Benzene
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct3017
Distinct (%)3.9%
Missing31455
Missing (%)29.1%
Infinite0
Infinite (%)0.0%
Mean3.3580293
Minimum0
Maximum455.03
Zeros12602
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:28.096316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16
median1.21
Q33.61
95-th percentile11.35
Maximum455.03
Range455.03
Interquartile range (IQR)3.45

Descriptive statistics

Standard deviation11.156234
Coefficient of variation (CV)3.3222565
Kurtosis695.20283
Mean3.3580293
Median Absolute Deviation (MAD)1.21
Skewness21.61702
Sum257157.88
Variance124.46157
MonotonicityNot monotonic
2024-12-30T18:46:28.228556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12602
 
11.7%
0.1 689
 
0.6%
0.01 541
 
0.5%
0.02 474
 
0.4%
0.12 439
 
0.4%
0.11 422
 
0.4%
0.03 420
 
0.4%
0.05 409
 
0.4%
0.04 403
 
0.4%
0.13 398
 
0.4%
Other values (3007) 59783
55.3%
(Missing) 31455
29.1%
ValueCountFrequency (%)
0 12602
11.7%
0.01 541
 
0.5%
0.02 474
 
0.4%
0.03 420
 
0.4%
0.04 403
 
0.4%
0.05 409
 
0.4%
0.06 374
 
0.3%
0.07 321
 
0.3%
0.08 337
 
0.3%
0.09 337
 
0.3%
ValueCountFrequency (%)
455.03 1
< 0.1%
454.85 1
< 0.1%
449.38 1
< 0.1%
448.59 1
< 0.1%
445.83 1
< 0.1%
443.63 1
< 0.1%
438.01 1
< 0.1%
435.9 1
< 0.1%
435.09 1
< 0.1%
432.94 1
< 0.1%

Toluene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8713
Distinct (%)12.6%
Missing38702
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean15.345394
Minimum0
Maximum454.85
Zeros10455
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:28.356210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.69
median4.33
Q317.51
95-th percentile64.934
Maximum454.85
Range454.85
Interquartile range (IQR)16.82

Descriptive statistics

Standard deviation29.348587
Coefficient of variation (CV)1.9125339
Kurtosis33.337213
Mean15.345394
Median Absolute Deviation (MAD)4.33
Skewness4.5983348
Sum1063942.2
Variance861.33958
MonotonicityNot monotonic
2024-12-30T18:46:28.480761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10455
 
9.7%
0.01 268
 
0.2%
0.02 199
 
0.2%
0.07 198
 
0.2%
0.03 179
 
0.2%
0.1 173
 
0.2%
0.04 173
 
0.2%
0.08 167
 
0.2%
0.06 161
 
0.1%
0.09 153
 
0.1%
Other values (8703) 57207
53.0%
(Missing) 38702
35.8%
ValueCountFrequency (%)
0 10455
9.7%
0.01 268
 
0.2%
0.02 199
 
0.2%
0.03 179
 
0.2%
0.04 173
 
0.2%
0.05 147
 
0.1%
0.06 161
 
0.1%
0.07 198
 
0.2%
0.08 167
 
0.2%
0.09 153
 
0.1%
ValueCountFrequency (%)
454.85 1
< 0.1%
454.12 1
< 0.1%
449.14 1
< 0.1%
448.87 1
< 0.1%
445.84 1
< 0.1%
443.63 1
< 0.1%
437.77 1
< 0.1%
435.94 1
< 0.1%
434.92 1
< 0.1%
433.02 1
< 0.1%

Xylene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1892
Distinct (%)8.3%
Missing85137
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean2.4234462
Minimum0
Maximum170.37
Zeros6083
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:28.604006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4
Q32.11
95-th percentile10.7315
Maximum170.37
Range170.37
Interquartile range (IQR)2.11

Descriptive statistics

Standard deviation6.4724085
Coefficient of variation (CV)2.6707457
Kurtosis119.56916
Mean2.4234462
Median Absolute Deviation (MAD)0.4
Skewness8.629739
Sum55492.07
Variance41.892072
MonotonicityNot monotonic
2024-12-30T18:46:28.745070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6083
 
5.6%
0.01 500
 
0.5%
0.02 423
 
0.4%
0.1 327
 
0.3%
0.03 297
 
0.3%
0.04 241
 
0.2%
0.05 188
 
0.2%
0.12 183
 
0.2%
0.06 180
 
0.2%
0.11 168
 
0.2%
Other values (1882) 14308
 
13.2%
(Missing) 85137
78.8%
ValueCountFrequency (%)
0 6083
5.6%
0.01 500
 
0.5%
0.02 423
 
0.4%
0.03 297
 
0.3%
0.04 241
 
0.2%
0.05 188
 
0.2%
0.06 180
 
0.2%
0.07 151
 
0.1%
0.08 148
 
0.1%
0.09 135
 
0.1%
ValueCountFrequency (%)
170.37 1
< 0.1%
137.45 1
< 0.1%
133.6 1
< 0.1%
132.97 1
< 0.1%
129.28 1
< 0.1%
125.18 1
< 0.1%
123.29 1
< 0.1%
116.62 1
< 0.1%
109.98 1
< 0.1%
109.23 1
< 0.1%

AQI
Real number (ℝ)

High correlation  Missing 

Distinct930
Distinct (%)1.1%
Missing21010
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean179.74929
Minimum8
Maximum2049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size844.2 KiB
2024-12-30T18:46:28.887074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile47
Q186
median132
Q3254
95-th percentile415
Maximum2049
Range2041
Interquartile range (IQR)168

Descriptive statistics

Standard deviation131.32434
Coefficient of variation (CV)0.73059726
Kurtosis8.5325443
Mean179.74929
Median Absolute Deviation (MAD)62
Skewness1.9300877
Sum15642682
Variance17246.082
MonotonicityNot monotonic
2024-12-30T18:46:29.024513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 615
 
0.6%
102 593
 
0.5%
106 587
 
0.5%
108 561
 
0.5%
100 560
 
0.5%
88 549
 
0.5%
98 547
 
0.5%
90 546
 
0.5%
92 546
 
0.5%
78 545
 
0.5%
Other values (920) 81376
75.3%
(Missing) 21010
 
19.4%
ValueCountFrequency (%)
8 1
 
< 0.1%
10 2
 
< 0.1%
13 2
 
< 0.1%
14 7
 
< 0.1%
15 5
 
< 0.1%
16 10
 
< 0.1%
17 13
 
< 0.1%
18 11
 
< 0.1%
19 36
< 0.1%
20 49
< 0.1%
ValueCountFrequency (%)
2049 1
< 0.1%
1917 1
< 0.1%
1842 1
< 0.1%
1747 1
< 0.1%
1719 1
< 0.1%
1672 1
< 0.1%
1646 1
< 0.1%
1630 1
< 0.1%
1613 1
< 0.1%
1595 1
< 0.1%

AQI_Bucket
Categorical

High correlation  Missing 

Distinct6
Distinct (%)< 0.1%
Missing21010
Missing (%)19.4%
Memory size844.2 KiB
Moderate
29417 
Satisfactory
23636 
Very Poor
11762 
Poor
11493 
Good
5510 

Length

Max length12
Median length9
Mean length8.3203677
Min length4

Characters and Unicode

Total characters724080
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate 29417
27.2%
Satisfactory 23636
21.9%
Very Poor 11762
 
10.9%
Poor 11493
 
10.6%
Good 5510
 
5.1%
Severe 5207
 
4.8%
(Missing) 21010
19.4%

Length

2024-12-30T18:46:29.164783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T18:46:29.279728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moderate 29417
29.8%
satisfactory 23636
23.9%
poor 23255
23.5%
very 11762
 
11.9%
good 5510
 
5.6%
severe 5207
 
5.3%

Most occurring characters

ValueCountFrequency (%)
o 110583
15.3%
r 93277
12.9%
e 86217
11.9%
a 76689
10.6%
t 76689
10.6%
y 35398
 
4.9%
d 34927
 
4.8%
M 29417
 
4.1%
S 28843
 
4.0%
i 23636
 
3.3%
Other values (8) 128404
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 724080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 110583
15.3%
r 93277
12.9%
e 86217
11.9%
a 76689
10.6%
t 76689
10.6%
y 35398
 
4.9%
d 34927
 
4.8%
M 29417
 
4.1%
S 28843
 
4.0%
i 23636
 
3.3%
Other values (8) 128404
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 724080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 110583
15.3%
r 93277
12.9%
e 86217
11.9%
a 76689
10.6%
t 76689
10.6%
y 35398
 
4.9%
d 34927
 
4.8%
M 29417
 
4.1%
S 28843
 
4.0%
i 23636
 
3.3%
Other values (8) 128404
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 724080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 110583
15.3%
r 93277
12.9%
e 86217
11.9%
a 76689
10.6%
t 76689
10.6%
y 35398
 
4.9%
d 34927
 
4.8%
M 29417
 
4.1%
S 28843
 
4.0%
i 23636
 
3.3%
Other values (8) 128404
17.7%

Interactions

2024-12-30T18:46:21.899275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:04.733028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.249907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.960926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.317871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.950469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.227304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.473679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.818673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.248349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.501003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.961969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.394081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.005319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:04.911680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.356858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.063238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.424831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.050436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.327058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.574541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.921084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.342807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.591321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.057269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.492882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.117203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.054626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.458780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.161194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.533572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.147744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.425409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.669579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.030208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.437784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.681443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.238189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.593766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.216774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.160036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.591343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.256919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.629052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.241222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.522698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.777952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.135349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.530910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.770655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.384998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.797377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.322600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.263618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.994421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.354588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.998874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.339797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.622318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.879418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.244828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.624577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.875495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.492983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.950627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.497810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.365788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.094429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.452071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.095999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.432627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.715955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.974512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.343030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.714009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.962488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.582664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.047877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.665423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.477123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.204041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.548640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.201849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.530182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.803099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.071673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.444480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.807267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.279339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.688167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.150924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.775859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.593257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.303262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.658412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.310123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.633837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.898586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.178276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.556179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.905864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.385818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.788308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.254109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:22.892558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.712263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.414599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.772391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.423334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.742491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.000763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.292772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.677785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.005673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.486529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.895467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.363693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:23.000700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.823159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.517494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.870420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.520725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.838746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.090493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.394158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.779853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.106466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.583969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:19.987189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.473618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:23.100491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:05.924049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.612657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:08.967973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.620380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:11.931983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.184469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.495592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.885545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.195772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.672524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.085838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.582164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:23.192405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.020653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.717359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.057792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.718297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.018444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.274117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.589911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:15.986587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.284670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.752979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.175494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.676883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:23.303742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:06.135692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:07.824719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:09.186064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:10.836771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:12.122866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:13.372608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:14.705167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:16.128096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:17.391721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:18.857459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:20.286017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T18:46:21.781940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-30T18:46:29.381173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AQIAQI_BucketBenzeneCONH3NONO2NOxO3PM10PM2.5SO2TolueneXylene
AQI1.0000.5840.3580.5530.5230.5200.5180.5260.2050.8960.8800.4060.3620.143
AQI_Bucket0.5841.0000.0290.1310.1920.1940.2180.2310.1280.5570.4690.1460.1320.042
Benzene0.3580.0291.0000.3390.2910.3440.4250.4000.0890.3640.3480.2750.8310.838
CO0.5530.1310.3391.0000.4190.4400.3730.4490.0430.5160.4750.2690.3750.340
NH30.5230.1920.2910.4191.0000.3590.4640.4060.1090.5230.5150.2650.3670.055
NO0.5200.1940.3440.4400.3591.0000.5750.805-0.1040.5200.5040.3160.3210.198
NO20.5180.2180.4250.3730.4640.5751.0000.7770.1800.5310.5150.3200.4520.275
NOx0.5260.2310.4000.4490.4060.8050.7771.0000.0120.5350.4980.3200.3850.279
O30.2050.1280.0890.0430.109-0.1040.1800.0121.0000.1270.1570.1950.0710.121
PM100.8960.5570.3640.5160.5230.5200.5310.5350.1271.0000.8980.4070.3620.039
PM2.50.8800.4690.3480.4750.5150.5040.5150.4980.1570.8981.0000.3400.3230.131
SO20.4060.1460.2750.2690.2650.3160.3200.3200.1950.4070.3401.0000.3850.235
Toluene0.3620.1320.8310.3750.3670.3210.4520.3850.0710.3620.3230.3851.0000.770
Xylene0.1430.0420.8380.3400.0550.1980.2750.2790.1210.0390.1310.2350.7701.000

Missing values

2024-12-30T18:46:23.467863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-30T18:46:23.778334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-30T18:46:24.229367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

StationIdDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
0AP0012017-11-2471.36115.751.7520.6512.4012.190.1010.76109.260.175.920.10NaNNaN
1AP0012017-11-2581.40124.501.4420.5012.0810.720.1215.24127.090.206.500.06184.0Moderate
2AP0012017-11-2678.32129.061.2626.0014.8510.280.1426.96117.440.227.950.08197.0Moderate
3AP0012017-11-2788.76135.326.6030.8521.7712.910.1133.59111.810.297.630.12198.0Moderate
4AP0012017-11-2864.18104.092.5628.0717.0111.420.0919.00138.180.175.020.07188.0Moderate
5AP0012017-11-2972.47114.845.2323.2016.5912.250.1610.55109.740.214.710.08173.0Moderate
6AP0012017-11-3069.80114.864.6920.1714.5410.950.1214.07118.090.163.520.06165.0Moderate
7AP0012017-12-0173.96113.564.5819.2913.9710.950.1013.90123.800.172.850.04191.0Moderate
8AP0012017-12-0289.90140.207.7126.1919.8713.120.1019.37128.730.252.790.07191.0Moderate
9AP0012017-12-0387.14130.520.9721.3112.1214.360.1511.41114.800.233.820.04227.0Poor
StationIdDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
108025WB0132020-06-2215.1030.982.5918.0420.6330.340.671.5025.841.288.32NaN38.0Good
108026WB0132020-06-2319.4842.373.0620.9423.9932.530.701.7228.211.655.93NaN44.0Good
108027WB0132020-06-2420.0546.633.7815.2819.0618.890.667.0841.561.147.24NaN59.0Satisfactory
108028WB0132020-06-2517.0339.643.2311.4214.6518.980.5711.3931.760.796.85NaN56.0Satisfactory
108029WB0132020-06-269.7919.8723.5116.5040.0225.090.6610.3430.190.936.37NaN50.0Good
108030WB0132020-06-278.6516.46NaNNaNNaNNaN0.694.3630.591.327.26NaN50.0Good
108031WB0132020-06-2811.8018.47NaNNaNNaNNaN0.683.4938.951.427.92NaN65.0Satisfactory
108032WB0132020-06-2918.6032.2613.65200.87214.2011.400.785.1238.173.528.64NaN63.0Satisfactory
108033WB0132020-06-3016.0739.307.5629.1336.6929.260.695.8829.641.868.40NaN57.0Satisfactory
108034WB0132020-07-0110.5036.507.7822.5030.2527.230.582.8013.101.317.39NaN59.0Satisfactory